Football Tactics and Match Analysis

As someone who spends a lot of time reading baseball and basketball analytics, I can’t help but take inspiration when working with football data. Expected Goals (xG) has been the most insightful metric to come out of this field in recent years. I tried to use it and other metrics to look at EPL attacks from a fresh perspective, using percentiles and breaking up xG into different components.

Removing penalties and non-shot incidents, I created the following table for all Premier League Teams in 2017-18:

The table with percentiles (which I lay out later) is much easier to read, but do familiarize yourself with definitions:

NPG: Non-penalty goals

xG: Expected Goals

xG/chance: Expected Goals per chance created

defPressure: Graded on a scale of 0-5 (5 being the highest) describing how much pressure the shooter is under. For example, 1 means a player stands a few yards away trying to block the shot, 3 means close contact with one defender (tugging shirt/chasing right behind), and 5 means the player is being crowded/blocked/tugged by multiple players.

Def Players: Number of defensive players in a direct line of goal from the shooting player.

Chance Volume: Its namesake, only taking into account the last shot from any sequence as per xG models.

Finish Quality: Subjective measure on the quality of shot, with a consistent framework for the ratings. Rated from 0-5, with 5 being perfect, 1 being a mishit and 3 being a standard shot on target.

The table describes teams while they attack. That means Bournemouth faced 2.74 defensive players behind the shots they took, rather than them putting 2.74 defensive players behind the ball on shots they conceded.

Now, you might have noticed that some of the differences on the charts are miniscule. The number of defensive players between the shooter and goal ranges from 248 per 100 shots to 274 per 100 shots. Is that significant? Football being such a low event sport (in front of goal) means variance plays a much larger role on a game to game basis than, say, basketball. The ‘favorite’ as per bookies’ definition only wins 55% of the time in football. Compare that to (roughly) 60% in baseball, 65% in American football, and 70% in basketball.

So I tried comparing these metrics to NBA offensive ratings (where 0.1 points per shot is the difference between a bottom and top ranked team) to see if the variance among these statistics is remotely significant:

Standard deviation of the following stats as a percentage of the median:

For context, defensive pressure and the number of defensive players explain 36% of the variance in xG per chance, while shot quality explains 44% of xG performance. So the metrics put together above essentially explore different aspects of a team’s attack.

Here’s the table with percentiles and the top 6 color coded:

NOTE: Higher percentile means better, in the case of defPressure and no. of defensive players less is better.

In contrast, Chelsea’s attack (like a lot of Italian teams, incidentally) also had good volume but only ranked in the 32nd percentile of quality, leaving them as the worst of the top 6. 10th percentile in defensive players behind the ball and the worst finishing of the top 6 too.

Manchester City, obviously, were ridiculous. Topped NPG, xG, volume AND quality. Faced the fewest defenders directly behind the ball on shots of the top 6 (despite facing some of the most packed defensive setups). Also miles ahead in the defensive pressure area:

Per @StrataBet data, Manchester City faced significantly less defensive pressure per shot than any team in the Premier League.

In contrast, neighbors United created great quality chances, but only sparingly, and found themselves under a ton of defensive pressure on those shots (5th percentile!). They still managed to outperform xG by the second highest margin, and got off some really good shots. I wonder how that changes with Mourinho’s appointment of a new coach for attacks.

Liverpool’s attack was elite, bodes well for their title challenge with a stronger side this year.

For Arsenal, attack was obviously not the problem.

Looking at those percentiles for the other 14 sides (Burnley, Bournemouth and Leicester especially) there’s a lot of information to be taken away by splitting up xG into its different components and examining how teams fare. Ultimately, though, those are descriptive measures compared to the all-encompassing tally of Expected Goals itself.

In the future, I’ll be putting out the defensive version of this table and expand into different leagues.